Health systems with duplicate services across multiple facilities in close proximity have an increased risk of unnecessary variation, greater costs, and suboptimal outcomes. By using data and analytics to identify high-performing programs and centralizing duplicated services at those locations, health systems can improve clinical and financial outcomes.
For example, an organization doesn’t likely need three facilities in the same city doing the same specialized services (e.g., three cardiac catheterization [cath] centers in the same vicinity). Duplication—whether in cath labs, specialty imaging, or cancer treatment—tends to result from competition between facilities, an interest from medical staff leadership, or a perceived need in the community, among many other reasons. All are legitimate motives, but, over time, duplication can result in sub-optimization of resources and performance.
Using outcomes and cost data, the health system can identify the top-performing center, whatever the service, from a cost and quality perspective and consolidate services accordingly. In so doing, the organization reduces costs associated with duplicated services and also consolidates its best clinicians, support services, and equipment into one place, ensuring patients the best care the system offers at the most appropriate cost.
While the financial and clinical benefits to organizations, and to patients, of centralizing services are clear, leadership may face operational and political challenges carrying out these consolidations. This article discusses the benefits and opportunities in consolidation, as well as common challenges.
Healthcare wastes an estimated $1 trillion annually in supply costs, unnecessary tests, and procedures that aren’t clinically indicated by best practices. Figure 1 shows the three classes of waste (case-rate utilization, within-case utilization, and efficiency), the percentages each makes up, and the waste subclasses. Within the subclasses are several instances where duplication of services within a health system can increase waste.
Health systems face four challenges when they centralize a service:
Not all health systems have extensive outcomes and cost data and an agile data platform (e.g., the Health Catalyst® Data Operating System) to leverage it. To make informed decisions about consolidation and measure subsequent performance, organizations must have both national and internal data and advanced analytics tools to apply the data to decision making. The consequences and implications of consolidation are too great to do otherwise. Imagine closing a program and displacing physicians and employees without complete confidence in the information upon which the decision was made.
A downside of centralization is that the health system must close a facility, or a major department, and move or eliminate groups of clinicians and employees. To do this successfully, leadership must align incentives among employees and create a strong argument for why such an impactful change is necessary. For example, if a health system closes a cath lab in one of its hospitals, that facility’s financial performance will likely suffer. Leadership shouldn’t, however, penalize the hospital administrator for that decline; they must instead reward extended leadership (with financial incentives) for doing the right thing for the patients and the overall organization versus fighting for preservation of their own hospital.
Secondly, the data must be used to generate understanding and support for a change in service or a closure. It must be demonstrated to all involved that the patient and the community benefit overall from the difficult decision.
Health systems likely face sunk costs when they centralize services—investments they’ve made in equipment and services that they may not get back if they discontinue a service at one or more hospitals. This loss may be a fact of centralization that leaders must accept for the long-term health of their organizations. This may, however, be mitigated by redeploying space for a more appropriate or productive service. For example, many spaces can now be better utilized to meet the growing demand for outpatient services.
When an organization closes facilities to consolidate services, its leadership must anticipate changes or challenges to its reputation within the community. Even though consolidation aims to improve delivery of care for specific services and throughout the system overall, community members may think less of a facility if it’s not performing a certain service (even if it was previously performing that surgery at a low volume with high costs). Again, the data should be used in this scenario to explain the change to community leaders and members. It is much harder to disagree with such a decision if people can understand the expected improvement.
Consolidation can be very difficult, and only systems with a data-driven foundation can do it well. Health systems can address each of the consolidation challenges above by prioritizing data and patient outcomes in their decision-making process. To succeed, they need data and analytics embedded in their processes so that each decision, from closing facilities and letting staff go to accepting sunk costs, is backed up by both national and internal data. Data is the only way leadership can justify difficult decisions because it objectively finds high-cost, low-performing facilities for certain services, allowing the organization to centralize around its low-cost, high-performing centers. The best data and analytics vendor systems to support variation reduction are the ones that determine variation at all levels—surgeon, facility, and program.
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